K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - RIS M4 - Citavi.

The members of the International Network for the Analysis of Intergenerational Relations proudly present the most recent issue of the jointly produced compendium "Generations, Intergenerational Relations and Generational Policy". This new version includes 17 languages: English, French, German, Italian, Spanish, Polish, Portuguese, Swedish, Hungarian, Turkish, Romanian, Lithuanian, Slovenian, Bosnian, Ukrainian, Russian and Chinese. The layout of the compendium is designed for using it to translate the specific concepts and terminology of research on generations and intergenerational relations from one language into (...) another. **** K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. (shrink)

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

Event-based prospective memory refers to remembering to execute planned actions in response to a target ProM cues. Encoding modality influences ProM performance; visual encoding has been studied more than auditory encoding. Further, it has not yet been examined whether different encoding may influence ProM performance in different encoding modalities. This study examines the effects of encoding modality, cue-encoding specificity, and encoding modes on event-based ProM tasks. In Experiment 1, cue specificity and encoding modality were manipulated as a within-groups encoding of (...) visual cues is more commonly and between-groups variable. Results revealed the facilitative effect of cue specificity on ProM performance. Also, with respect to encoding modality, participants showed better performance when receiving auditory instructions compared with the visual encoding condition. In Experiment 2, as in Experiment 1, cue specificity and encoding modality were manipulated. Encoding mode was added as a new between-group variable. Result revealed that there was a significant interaction between encoding modality and encoding modes. Visual implementation intention encoding was a more effective method for improving ProM performance compared with visual standard encoding. Furthermore, there was a significant interaction between cue-encoding specificity and encoding modes. Implementation intention encoding enhances ProM performance in non-specific cue-encoding conditions. Overall, the present study found that auditory encoding modality showed superior ProM performance compared with visual encoding, although implementation intention had facilitative on ProM performance regardless of the encoding modalities, and there was better ProM performance under specific encoding compared with non-specific encoding, and implementation intention had a facilitative effect on ProM performance in the non-specific condition. (shrink)

本书收录《基督教与道教比较论纲》、《天主教成圣观的天人合一幅度》、《论早期基督宗教的性爱伦理观》等20篇论文。.

State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks. However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of (...) three facial expressions. To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp. (shrink)